The NExT Enable group focuses on creating technologies to help restore capabilities to people living with disabilities.
The Systems Research Group is devoted to significantly extending the state of the art in distributed systems and operating systems. Our aim is to make systems secure, scalable, fault-tolerant, manageable, and fast
The Audio and Acoustics group conducts research in audio processing and speech enhancement, 3D audio perception and technologies, devices for audio capture and rendering, array processing, information extraction from audio signals.
Our mission is to harvest and curate the wealth of knowledge encoded in language: people, content, things, connections, and activities. We mobilize research and advanced technology for the Technology arm of MSR by adapting, developing and integrating state-of-the-art technology from NLP, text mining, machine learning, knowledge extraction, and knowledge representation, while building end to end interactive knowledge experiences in close collaboration with partners across MSR and product teams.
The Knowledge Mining (KM) group at Microsoft Research Asia aims to understand and serve the world through knowledge discovery and data mining. It consists of a team of interdisciplinary researchers spanning data mining, machine learning, natural language processing, information retrieval and social computing areas.
Studio 99 is a new gallery space at Microsoft Research. Its goals are to express the creative talents of the MSR community and stimulate interesting conversations about the relationship between art and science. Many of the greatest scientists have also been artists, and the spark of creativity links both fields. By providing a space for science and art to interact, Studio 99 hopes to inspire new kinds of human expression, both scientific and artistic.
Multimedia Search and Mining (MSM) group focuses on pattern analysis and extraction for multimedia understanding, search, and data mining. We are working on research problems in search-based image annotation, large scale visual (image and video) indexing and search, sketch-based image search, object recognition with 3D structures, social multimedia analytics, etc.
Our goal is to extract biological and medical knowledge from text. Natural Language Processing tools and techniques are used in combination with biological resources.
Our mission is to explore next-generation computing systems that are scalable, efficient, robust, and easy to program.
In the System Algorithms (SysAlgo) Research Group, we focus on problems at the intersection of systems, networking, and algorithms research. We study the algorithmic foundations of the systems that drive today's computing (cloud computing, data centers, large-scale distributed systems, mobile computing, etc); and we apply our expertise in practice to advance the state of the art in applied algorithm design, and to deliver highly efficient, scalable, and robust solutions to our product groups.
In recent years, we have seen dramatic improvements in machine learning, knowledge mining, graph database, and crowdsourcing that are providing search engines with new capabilities to perform deeper data and text processing and understanding. Web Search and Data Management Group is performing cutting edge research in these related areas and developing new capabilities to empower next generation search engines and intelligent applications.
Computer Human Interactive Learning
The team of scientists in Bing that develop speech and language technologies
We are conducting numerous projects aimed at improving web search. Our projects range from developing core systems infrastructure, to developing novel algorithms and heuristics for ranking and classifying web pages, to study basic properties of the web at large, to mining query logs for temporal patterns.
We are currently investigating various topics related to the correctness and performance of software systems, especially in the area of concurrent systems. We place a high value on producing tools and methodologies that can be used by software developers and researchers.
We are currently investigating a broad spectrum of topics in security, cryptography, and privacy. These topics range from fundamental research on privacy in the context of statistical databases to new systems mechanisms for realizing security in operating systems to mitigating and preventive measures against worms and viruses.
We are currently investigating various topics related to computer architecture (multicore, manycore, transaction memory, etc.), hardware accelerators, systems architecture (storage, nonvolatile memory, etc.), including software and hardware components, and graphics. We strive to understand and optimize systems and system interactions, enabling new paradigms, accelerators, and research platforms. As a result, we build hardware and software systems that facilitate research in a variety of areas.
Algorithms and theory research at the Silicon Valley Campus is motivated equally by the goals of having significant impact on the real world and by advancing the state of the art in pure research. To this end, we pursue a wide variety of projects over many diverse research areas, most recently including graph algorithms, database privacy, approximation algorithms, algorithmic mechanism design, cryptography, algorithms for large data sets and the theory of distributed computing, among others.
Our research activities in the distributed system area range from protocols and algorithms to decentralized architectures and services. We address fundamental issues such as scalability, fault-tolerance, security, manageability, and mobility. We also explore the basic building blocks for distributed systems including networks and operating systems.
Researchers working on computational aspects of market design.
Web platforms such as Amazon’s Mechanical Turk are revolutionizing our ability to conduct human behavioral experiments of the kind historically performed in physical labs. Such “virtual lab” experiments allow for individual-level psychology and economics experiments to be carried out with unprecedented scale and speed, and also permit larger and more complex “networked” experiments on topics such as cooperation, learning, and collective problem solving.
With increasingly more data on every aspect of our daily activities – from what we buy, to where we travel, to who we know – we are able to measure human behavior with precision largely thought impossible just a decade ago. Lying at the intersection of computer science, statistics and the social sciences, the emerging field of computational social science uses large-scale demographic, behavioral and network data to address longstanding questions in sociology, economics, politics, and beyond.
Research of the Machine Learning group at MSR-NYC spans a wide variety of topics within theoretical and applied machine learning, including learning from interactive data (e.g., contextual bandits), large-scale machine learning, and convex optimization.
We focus on solving some of the most pressing, real-world problems in computer science.